Complexity of Weak Bisimilarity and Regularity for BPA and BPP

It is an open problem whether weak bisimilarity is decidable for Basic Process Algebra (BPA) and Basic Parallel Processes (BPP). A PSPACE lower bound for BPA and NP lower bound for BPP have been demonstrated by Stribrna. Mayr achieved recently a result, saying that weak bisimilarity for BPP is Π^p₂-hard. We improve this lower bound to PSPACE, moreover for the restricted class of normed BPP.Weak regularity (finiteness) of BPA and BPP is not known to be decidable either. In the case of BPP there is a Π^p₂-hardness result by Mayr, which we improve to PSPACE. No lower bound has previously been established for BPA. We demonstrate DP-hardness, which in particular implies both NP and co-NP-hardness.In each of the bisimulation/regularity problems we consider also the classes of normed processes.Note: full version of the paper appears as [18].     

Simulation Preorder over Simple Process Algebras

We consider the problem of simulation preorder/equivalence between infinite-state processes and finite-state ones. First, we describe a general method how to utilize the decidability of bisimulation problems to solve (certain instances of) the corresponding simulation problems. For certain process classes, the method allows us to design effective reductions of simulation problems to their bisimulation counterparts and some new decidability results for simulation have already been obtained in this way. Then we establish the decidability border for the problem of simulation preorder/equivalence between infinite-state processes and finite-state ones w.r.t. the hierarchy of process rewrite systems. In particular, we show that simulation preorder (in both directions) and simulation equivalence are decidable in EXPTIME between pushdown processes and finite-state ones. On the other hand, simulation preorder is undecidable between PA and finite-state processes in both directions. These results also hold for those PA and finite-state processes which are deterministic and normed, and thus immediately extend to trace preorder. Regularity (finiteness) w.r.t. simulation and trace equivalence is also shown to be undecidable for PA. Finally, we prove that simulation preorder (in both directions) and simulation equivalence are intractable between all classes of infinite-state systems (in the hierarchy of process rewrite systems) and finite-state ones. This result is obtained by showing that the problem whether a BPA (or BPP) process simulates a finite-state one is PSPACE-hard and the other direction is co -hard; consequently, simulation equivalence between BPA (or BPP) and finite-state processes is also co -hard.     

Deciding co-observability is PSPACE-complete

In this note, we reduce the deterministic finite-state automata intersection problem to the problem of deciding co-observability for regular languages using a polynomial-time many-one mapping. This demonstrates that the problem of deciding co-observability for languages marked by deterministic finite-state automata is PSPACE-complete. We use a similar reduction to reduce the deterministic finite-state automata intersection problem to deciding other versions of co-observability introduced in a previous paper. These results imply that the co-observability of regular languages most likely cannot be decided in polynomial time unless we make further restrictions on the languages. These results also show that deciding decentralized supervisor existence is PSPACE-complete and therefore probably intractable.     

Efficiently identifying deterministic real-time automata from labeled data

We develop a novel learning algorithm RTI for identifying a deterministic real-time automaton (DRTA) from labeled time-stamped event sequences. The RTI algorithm is based on the current state of the art in deterministic finite-state automaton (DFA) identification, called evidence-driven state-merging (EDSM). In addition to having a DFA structure, a DRTA contains time constraints between occurrences of consecutive events. Although this seems a small difference, we show that the problem of identifying a DRTA is much more difficult than the problem of identifying a DFA: identifying only the time constraints of a DRTA given its DFA structure is already NP-complete. In spite of this additional complexity, we show that RTI is a correct and complete algorithm that converges efficiently (from polynomial time and data) to the correct DRTA in the limit. To the best of our knowledge, this is the first algorithm that can identify a timed automaton model from time-stamped event sequences.     

Covering sharing trees: a compact data structure for parameterized verification

The control state reachability problem is decidable for well-structured infinite-state systems like (Lossy) Petri Nets, Vector Addition Systems, and broadcast protocols. An abstract algorithm that solves the problem is the backward reachability algorithm of [1, 21 ]. The algorithm computes the closure of the predecessor operator with respect to a given upward-closed set of target states. When applied to this class of verification problems, symbolic model checkers based on constraints like [7, 26 ] suffer from the state explosion problem.In order to tackle this problem, in [13] we introduced a new data structure, called covering sharing trees, to represent in a compact way collections of infinite sets of system configurations. In this paper, we will study the theoretical complexity of the operations over covering sharing trees needed in symbolic model checking. We will also discuss several optimizations that can be used when dealing with Petri Nets. Among them, in [14] we introduced a new heuristic rule based on structural properties of Petri Nets that can be used to efficiently prune the search during symbolic backward exploration. The combination of these techniques allowed us to turn the abstract algorithm of [1, 21 ] into a practical method. We have evaluated the method on several finite-state and infinite-state examples taken from the literature [2, 18 , 20 , 30 ]. In this paper, we will compare the results we obtained in our experiments with those obtained using other finite and infinite-state verification tools.     

Preemptive scheduling with release times and deadlines

We consider the problem of deciding if there is a feasible preemptive schedule for a set of n independent tasks with release times and deadlines on m identical processors. The general problem is known to be solvable in O(n ³) time. In this paper, we study special cases for which faster algorithms exist. We introduce the notion of monotone schedules and study their properties. These properties are then exploited to devise fast algorithms for two special cases—the nested task systems and the non-overlapping task systems. We give an O(n log mn) time algorithm and an O(n log n+mn) time algorithm for nested task systems and non-overlapping task systems, respectively. Our algorithms generate at most O(n) and O(mn) preemptions for nested task systems and nonoverlapping task systems, respectively.Research supported in part by the ONR grant N00014-87-K-0833.     

An efficient dynamic programming algorithm for a special case of the capacitated lot-sizing problem

In this paper we consider the capacitated lot-sizing problem (CLSP) with linear costs. It is known that this problem is NP-hard, but there exist special cases that can be solved in polynomial time. We derive a new O(T²) algorithm for the CLSP with non-increasing setup costs, general holding costs, non-increasing production costs and non-decreasing capacities over time, where T is the length of the model horizon. We show that in every iteration we do not consider more candidate solutions than the O(T²) algorithm proposed by [Chung and Lin, 1988. Management Science 34, 420–6]. We also develop a variant of our algorithm that is more efficient in the case of relatively large capacities. Numerical tests show the superior performance of our algorithms compared to the algorithm of [Chung and Lin, 1988. Management Science 34, 420–6].     

Keeping track of the latest gossip in a distributed system

Summary.  We tackle a natural problem from distributed computing, involving time-stamps. Let ?={p ₁, p ₂, …, p _N } be a set of computing agents or processes which synchronize with each other from time to time and exchange information about themselves and others. The gossip problem is the following: Whenever a set P⊆? meets, the processes in P must decide amongst themselves which of them has the latest information, direct or indirect, about each agent p in the system. We propose an algorithm to solve this problem which is finite-state and local. Formally, this means that our algorithm can be implemented as an asynchronous automaton. Received: December 1994 / Accepted: July 1996     

A Tight Lower Bound for Computing the Diameter of a 3D Convex Polytope

The diameter of a set P of n points in ℝ ^d is the maximum Euclidean distance between any two points in P. If P is the vertex set of a 3-dimensional convex polytope, and if the combinatorial structure of this polytope is given, we prove that, in the worst case, deciding whether the diameter of P is smaller than 1 requires Ω(nlog n) time in the algebraic computation tree model. It shows that the O(nlog n) time algorithm of Ramos for computing the diameter of a point set in ℝ³ is optimal for computing the diameter of a 3-polytope. We also give a linear time reduction from Hopcroft’s problem of finding an incidence between points and lines in ℝ² to the diameter problem for a point set in ℝ⁷.     

Deciding probabilistic bisimilarity over infinite-state probabilistic systems

We prove that probabilistic bisimilarity is decidable over probabilistic extensions of BPA and BPP processes. For normed subclasses of probabilistic BPA and BPP processes we obtain polynomial-time algorithms. Further, we show that probabilistic bisimilarity between probabilistic pushdown automata and finite-state systems is decidable in exponential time. If the number of control states in PDA is bounded by a fixed constant, then the algorithm needs only polynomial time. The work has been supported by the research centre Institute for Theoretical Computer Science (ITI), project No. 1M0545.     

A spectral characterization of H-optimal feedback performance and its efficient computation

We study the spectral properties of a ‘Toeplitz⁺ Hankel’ operator which arises in the context of the mixed-sensitivity H^∞-optimization problem and whose largest eigenvalue characterizes the optimal achievable performance ε₀. The existence of such an operator was first shown by Verma and Jonckheere [26], who also'noted the potential numerical advantage of computing eo through its eigenvalue characterization rather than through the ε-iteration. Here, we investigate this operator in detail, with the objective of efficiency computing its spectrum. We define an ‘adjoint’ linear-quadratic problem that involves the same ‘Toeplitz⁺ Hankel’ operator, as shown by Jonckheere and Silverman [13–16]. Consequently, a finite polynomial algorithm allows ε₀ to be characterized as simply as the largest root of a polynomial. Finally, a computationally more attractive state space algorithm emerges from the H^t8/LQ relationship. This algorithm yields a very good accuracy evaluation of the performance ε₀ by solving just one algebraic Riccati equation. Thorough exploitation of this algorithm results in a drastic computation reduction with respect to the standard e-iteration.     

Approximate algorithms for the knapsack problem on parallel computers

Computing an optimal solution to the knapsack problem is known to be NP-hard. Consequently, fast parallel algorithms for finding such a solution without using an exponential number of processors appear unlikely. An attractive alternative is to compute an approximate solution to this problem rapidly using a polynomial number of processors. In this paper, we present an efficient parallel algorithm for finding approximate solutions to the 0–1 knapsack problem. Our algorithm takes an , 0 < < 1, as a parameter and computes a solution such that the ratio of its deviation from the optimal solution is at most a fraction of the optimal solution. For a problem instance having n items, this computation uses O(n^5/2/^3/2) processors and requires O(log³n + log²nlog(1/)) time. The upper bound on the processor requirement of our algorithm is established by reducing it to a problem on weighted bipartite graphs. This processor complexity is a significant improvement over that of other known parallel algorithms for this problem.     

Simulating perfect channels with probabilistic lossy channels

We consider the problem of deciding whether an infinite-state system (expressed as a Markov chain) satisfies a correctness property with probability 1. This problem is, of course, undecidable for general infinite-state systems. We focus our attention on the model of probabilistic lossy channel systems consisting of finite-state processes that communicate over unbounded lossy FIFO channels. Abdulla and Jonsson have shown that safety properties are decidable while progress properties are undecidable for non-probabilistic lossy channel systems. Under assumptions of “sufficiently high” probability of loss, Baier and Engelen have shown how to check whether a property holds of probabilistic lossy channel system with probability 1. In this paper, we consider a model of probabilistic lossy channel systems, where messages can be lost only during send transitions. In contrast to the model of Baier and Engelen, once a message is successfully sent to channel, it can only be removed through a transition which receives the message. We show that checking whether safety properties hold with probability 1 is undecidable for this model. Our proof depends upon simulating a perfect channel, with a high degree of confidence, using lossy channels.     

A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes

A critical issue for the application of Markov decision processes (MDPs) to realistic problems is how the complexity of planning scales with the size of the MDP. In stochastic environments with very large or infinite state spaces, traditional planning and reinforcement learning algorithms may be inapplicable, since their running time typically grows linearly with the state space size in the worst case. In this paper we present a new algorithm that, given only a generative model (a natural and common type of simulator) for an arbitrary MDP, performs on-line, near-optimal planning with a per-state running time that has no dependence on the number of states. The running time is exponential in the horizon time (which depends only on the discount factor and the desired degree of approximation to the optimal policy). Our algorithm thus provides a different complexity trade-off than classical algorithms such as value iteration—rather than scaling linearly in both horizon time and state space size, our running time trades an exponential dependence on the former in exchange for no dependence on the latter.Our algorithm is based on the idea of sparse sampling. We prove that a randomly sampled look-ahead tree that covers only a vanishing fraction of the full look-ahead tree nevertheless suffices to compute near-optimal actions from any state of an MDP. Practical implementations of the algorithm are discussed, and we draw ties to our related recent results on finding a near-best strategy from a given class of strategies in very large partially observable MDPs (Kearns, Mansour, & Ng. Neural information processing systems 13, to appear).     

Pushdown module checking

Model checking is a useful method to verify automatically the correctness of a system with respect to a desired behavior, by checking whether a mathematical model of the system satisfies a formal specification of this behavior. Many systems of interest are open, in the sense that their behavior depends on the interaction with their environment. The model checking problem for finite-state open systems (called module checking) has been intensively studied in the literature. In this paper, we focus on open pushdown systems and we study the related model-checking problem (pushdown module checking, for short) with respect to properties expressed by CTL and CTL ^* formulas. We show that pushdown module checking against CTL (resp., CTL ^*) is 2Exptime-complete (resp., 3Exptime-complete). Moreover, we prove that for a fixed CTL or CTL ^* formula, the problem is Exptime-complete.     

Finding the closed partition of a planar graph

In this paper we consider the problem of finding aclosed partition in a directed graph. This problem has applications in concurrent probabilistic program verification. The best sequential algorithm known for this problem runs inO(mn) time wherem is the number of directed edges andn is the number of vertices in the given digraph. In this paper we present a linear-time sequential algorithm to solve the closed partition problem for planar digraphs that arecompact. We then build on this algorithm to obtain an O(n^1.5)-time sequential algorithm to solve the closed partition problem for a general planar digraph.This work was supported in part by NSF Grant CCR 89-10707.     

On Deciding Readiness and Failure Equivalences for Processes

In this paper, we study the complexity of deciding readiness and failure equivalences for finite state processes and recursively defined processes specified by normed context-free grammars (CFGs) in Greibach normal form (GNF). The results are as follows: (1) Readiness and failure equivalences for processes specified by normed GNF CFGs are both undecidable. For this class of processes, the regularity problem with respect to failure or readiness equivalence is also undecidable. Moreover, all these undecidability results hold even for locally unary processes. In the unary case, these problems become decidable. In fact, they are Π^p₂-complete, We also show that with respect to bisimulation equivalence, the regularity for processes specified by normed GNF CFGs is NL-complete. (2) Readiness and failure equivalences for finite state processes are PSPACE-complete. This holds even for locally unary finite state processes. These two equivalences are co-NP-complete for unary finite state processes. Further, for acyclic finite state processes, readiness and failure equivalences are co-NP-complete and they are NL-complete in the unary case. (3) For finite tree processes, we show that finite trace, readiness, and failure equivalences are all L-complete. Further, the results remain true for the unary case. Our results provide a complete characterization of the computational complexity of deciding readiness and failure equivalences for several important classes of processes.     

The unification problem for confluent right-ground term rewriting systems

The unification problem for term rewriting systems (TRSs) is the problem of deciding, for a given TRS R and two terms M and N, whether there exists a substitution θ such that Mθ and Nθ are congruent modulo R (i.e., Mθ↔_R^*Nθ). In this paper, the unification problem for confluent right-ground TRSs is shown to be decidable. To show this, the notion of minimal terms is introduced and a new unification algorithm for obtaining a substitution whose range consists of minimal terms is proposed. Our result extends the decidability of unification for canonical (i.e., terminating and confluent) right-ground TRSs given by Hullot [Proceedings of the 5th Conference on Automated Deduction, LNCS, vol. 87, 1980, p. 318] in the sense that the termination condition can be omitted.     

Orienting rewrite rules with the Knuth–Bendix order

We consider two decision problems related to the Knuth–Bendix order (KBO). The first problem is orientability: given a system of rewrite rules R, does there exist an instance of KBO which orients every ground instance of every rewrite rule in R. The second problem is whether a given instance of KBO orients every ground instance of a given rewrite rule. This problem can also be reformulated as the problem of solving a single ordering constraint for the KBO. We prove that both problems can be solved in the time polynomial in the size of the input. The polynomial-time algorithm for orientability builds upon an algorithm for solving systems of homogeneous linear inequalities over integers. We show that the orientability problem is P-complete. The polynomial-time algorithm for solving a single ordering constraint does not need to solve systems of linear inequalities and can be run in time O(n²). Also we show that if a system is orientable using a real-valued instance of KBO, then it is also orientable using an integer-valued instance of KBO. Therefore, all our results hold both for the integer-valued and the real-valued KBO.